# NURS 8201 Correlations

## Sample Answer for NURS 8201 CorrelationsIncluded After Question

### HE ASSIGNMENT: (2–3 PAGES)

Answer the following questions using the Week 6 Correlations Exercises SPSS Output provided in this week’s Learning Resources.

1. What is the strongest correlation in the matrix? (Provide the correlation value and the names of variables)
2. What is the weakest correlation in the matrix? (Provide the correlation value and the names of variables)
3. How many original correlations are present on the matrix?
4. What does the entry of 1.00 indicate on the diagonal of the matrix?
5. Indicate the strength and direction of the relationship between body mass index (BMI) and physical health component subscale.
6. Which variable is most strongly correlated with BMI? What is the correlational coefficient? What is the sample size for this relationship?
7. What is the mean and standard deviation for BMI and doctor visits?
8. What is the mean and standard deviation for weight and BMI?
9. Describe the strength and direction of the relationship between weight and BMI.
10. Describe the scatterplot. What information does it provide to a researcher?

Reminder: The College of Nursing requires that all papers submitted include a title page, introduction, summary, and references. The Sample Paper provided at the Walden Writing Center provides an example of those required elements (available at https://academicguides.waldenu.edu/writingcenter/templates/general#s-lg-box-20293632Links to an external site.). All papers submitted must use this formatting.

## Title: NURS 8201 Correlations

Children immunization can be seen to be high even though some parents insist that the persistent usage of vaccines used for immunization may be the ones responsible for autism. The research question is does immunization causes autism. The hypothesis that can be developed is ;( 1) the null hypothesis the administration of many vaccines weakens the body’s immune system and (2) the alternative hypothesis of how vaccines cause autism in an individual.

Autism is the independent variable, while immunization is the dependent variable. One can either have autism with or without immunization; therefore, autism does not depend on immunization. Autism can be developed when there is a combination of non-genetic and genetic or an influence of the environment (Gerber and Offit, 2009). Additionally, autism can be developed when the parents are advanced in age, birth complications, and pregnancies, such as when there is an occurrence of extremely premature children who have a birth weight that is low in addition to when there is an existence of multiple pregnancies which are when an individual gets twins, triplets, etc. Notably, when there is less spacing from one child to another while giving birth can also be one of the factors that can cause autism.

The other factor that can make one develop autism is that autism can be a disability that can be developed depending on the way the brain usually functions. The ingredients of vaccines, more so thimerosal, a preservative mercury-based that can be used for preventive vaccines, do not influence the immune system (Chen and DeStefano, 2001). In addition, an individual can have autism since it runs in the family due to their genetic makeup.

### References

Andrews, N., Miller, E., Taylor, B., Lingam, R., Simmons, A., Stowe, J., & Waight, P. (2002). Recall bias, MMR, and autism. Archives of disease in childhood

Chen, R. T., & DeStefano, F. (2001). Vaccine adverse events: causal or coincidental?

Gerber, J. S., & Offit, P. A. (2009). Vaccines and autism: a tale of shifting hypotheses. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America

Taylor, B., Miller, E., & Farrington, P. (2000). Autism and measles, mumps, and rubella vaccine. The Lancet.

33(6), 973-983. https://onlinelibrary.wiley.com/doi/full/10.1002/jts.22563

## Title: NURS 8201 Correlations

Problem statement: Is there a positive nursing outcome when implementing an educational bridge program for ICU transition?

Independent variable- educational bridge program

Dependent variable- positive outcomes

Null hypothesis- There are no changes in nursing outcome when implementing an educational program for bridging into ICU transition because most nurses are not receptive to change.

Alternative hypothesis- There is a positive nursing outcome when implementing educational bridge program for ICU transition as a result of quality patient care.

Change is inevitable as many would like to say and just like being a nurse, changes occur every day and new research is being done to create evidenced based practices that would encourage quality patient care. Being a nurse for about 6 years now, I have encountered many changes through policies that needed to occur to assist in quality patient care. Also, working on Guam with an expanding multicultural population and within a government hospital, it is advisable that we follow and implement updated healthcare policies. One of the main new practice problems that we are currently encountering is the idea that nurses who wish to transfer to the intensive care unit is not receiving the proper education and transitional bridge into the ICU setting.

The critical care unit is a specialty unit that requires advanced skills and educational training and essentials of critical care orientation in order to attain acute care assessment skills. Due to an increase in the heavy and acutely ill patient population our ICU is trying to implement is starting up an educational bridge program for nurses who wish to transition into the ICU setting. However, just like change is bound to happen, not many nurses are receptive to receiving changes afraid of punitive actions towards mistakes, and may be afraid to reach out to other management about competency. In order to prioritize patient safety, the complexity of patients’ condition and treatment process in the intensive care unit predisposes patients to more hazardous events. Therefore, correlation studies should be done to determine if there are positive outcomes when implementing an educational bridge to transition into the ICU setting.

Correlation research can be defined as research design that investigates relationships between variables without the researchers controlling or manipulating them (Bhandari, 2021). In a randomized control study done by Amiri, Khademian, & Nikandish (2018), with a randomized experimental and control groups consisting of distribution of pamphlets about culture of safety and hospital surveys with a pre- and post- test; empowering nurses and supervisors through educational programs on patient safety could improve patient care outcomes. The epidemiology of errors included medication doses, prescription, and transcription, poor communication lack of knowledge and inadequate training are the main causes of nursing errors in the ICU. As a result, high morbidity and mortality associated with medication errors indicate the importance of promoting safety through educational bridging to transition in the ICU. Significant improvements were observed in promoting organizational learning, continuous improvements, and promoting patient safety as a result of post-test from the control group that received the educational bridge of transition into the ICU with a positive correlative dimension (72.3% positive responses).

In the end, it all comes down to knowing the foundation of nursing and from there build clinical performance that is suitable for the ICU setting. In article done by Shogi et.al. (2019), the gap between educational and clinical practice continues to be a challenge for educational experts therefore, this qualitative interview analysis study was conducted among nurses and administrators there is a need to bridge the gap between theory and practice. The theory and practice gap has been a consistent nursing problem encountered by both new graduates and experienced nurses. Incompatibility of theoretical education with the performance of nurses in the clinical setting can lead to inappropriate use of scientific resource coupled with adherence to conventional traditional methods in the healthcare setting resulting in ineffective nursing practice. Evidence based practices are coupled with quality patient care therefore, the link between knowledge and practice is vital for supporting clinical decision making and development in the nursing profession. This theory gap not only reduces motivation but also lead to a decrease in quality patient care. Mentorship and preceptorship would aid in bridging this gap to work efficiently and meet the ever changing healthcare needs.

Our ICU management is now re-introducing current theories in practice patients and validate scientific evidence with the inclusion of other fundamental measures such as cultivating positive attitudes, re-orienting nursing studies, and education. Effective education is key to implantation of quality care. Nurses serve as the bridge to management in promoting educational clinical guidelines based on their local facilities to share expectations.

### Reference(s):

Amir, M., Khademian, Z., & Kikandish, R. (2018). The effect of nurse empowerment

educational program on patient safety culture: A randomized control trial. BMC Medical Education. 18(158). Retrieved from https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-018-1255-6

Bhandari, R. (2021). An introduction to correlational research. Scribbr Statistics. Retrieved from

the theory-practice gap from the perspective of nursing expert. Science Direct. 5(9). Retrieved from https://www.sciencedirect.com/science/article/pii/S2405844019361638

Amir, M., Khademian, Z., & Kikandish, R. (2018). The effect of nurse empowerment

educational program on patient safety culture: A randomized control trial. BMC Medical Education. 18(158). https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-018-1255-6

Nakagawa, S., Johnson, P. C., & Schielzeth, H. (2017). The coefficient of determination R 2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of the Royal Society Interface14(134), 20170213. https://doi.org/10.1098/rsif.2017.0213

Bartlett, N., Langerak, S., Lucas, L., Archibald, J., Robbins, T., Thompson, M., … & Sparks, A. (2019). Intensive Care to Intermediate Care Bridge Program. https://core.ac.uk/reader/270181500

## Title: NURS 8201 Correlations

Correlational studies or research plays a crucial role in helping researchers gain insight into how particular study variables are related. Through correlational statistics or studies, individuals get to know the strength of a correlation between the variables, and through careful interpretation, a researcher can have an idea if there is a statistically relevant relationship or association (Janse et al.,2021). Therefore, the purpose of this assignment is to explore how to interpret results obtained through a correlational analysis. As such, a correlation SPSS output will be evaluated, and various questions will answered.

## The Strongest Correlation In the Matrix

In the provided output, the strongest correlation is between Body Mass Index and weight pounds. It is evident that the Pearson correlation coefficient for the relationship between BMI and Weight-pounds is 0.937. It is important to note that this relationship is significant as a two-tailored significance has been pegged at 0.01 (Makowski et al.,2020).

## The Weakest Correlation In the Matrix

It is also important to explore the weakest correlation in the matrix. From the output, the weakest correlation is the correlation between the Body Mass Index and SF12: Mental Health Component score, standardized. The correlation value is -0.078, which indicates a weak correlation.

## The Number of Original Correlations In the Matrix

From the provided output, there are a total of nine correlations. The correlation includes Number of doctor visits, past 12 months and Body Mass Index, Number of doctor visits, past 12 months, and SF12: physical health component score. The next is the Number of doctor visits, past 12 months, and SF12: Mental Health Component Score, standardized; the BMI and SF12: Physical Health Component Score standardized, and Body Mass Index and Weight-pounds. The next correlations are BMI and Weight, SF12: Physical Health Component Score, standardized, and SF12: Mental Health Component Score, standardized. The other includes SF12:Physical Health Component Score, standardized and Body Mass Index, SF12: Mental Health Component Score, standardized, and Number of doctor visits, past 12 months.

## What the Entry of 1.00 Indicates on the Diagonal of the Matrix

The entry of 1.00 on the diagonal matrix indicates that each variable is in perfect correlation with itself (Pandey, 2020). It is easily observable as it is indicated from the top left to the bottom right of the main diagonal.

## The Strength and Direction of The Relationship Between BMI and Physical Health

### Component Subscale

The strength of the correlation between body mass index and the physical health component subscale is -0.134. In terms of direction, it is negative, which implies that when the BMI increases, the physical health component subscale decreases. It implies that the two variables are inversely related. In addition, it shows a weak relationship.

## The Variable That Is Most Strongly Correlated With BMI, Coefficient, and Sample Size

From the SPSS output, the variable that is most strongly correlated with Body Mass Index is the Weigh-pounds. The correlational coefficient between the two variables is 0.937. In addition, the sample size for the relationship between Body Mass Index and Weight-pounds is 970. The correlation indicates a very strong positive relationship. The direction is positive, which shows that when the Body Mass Index is high, there is a substantial increase in the weight in pounds. In addition, the strong positive correlation is an indication that a positive and close connection exists between weight in pounds and body mass index.

## The Mean and Standard Deviation for BMI and Doctor Visits

From the output, the mean for Body Mass Index is 29.222, with a standard deviation of 7.379. In addition, the mean for the Number of Doctor Visits in the past 12 months is 6.80, with a standard deviation of 12.720.

## The Mean and Standard Deviation for Weight and BMI

From the provided output, the mean for BMI is 29.22, with a standard deviation of 7.38. besides, the mean of weight-pounds is 171.462, with a standard deviation of 7.38.

## The Strength and Direction of the Relationship Between Weight and BMI

The relationship between weight and BMI is positive and very strong, as the correlation coefficient is 0.937. The positive sign is an indication that when BMI increases, the weight also increases notably.

## Description of Scatterplot and the Information It Provides to the Researcher

Scatterplots are applied to help show the connection between variables. The scatterplot provided in the output displays a relationship between weight and Body Mass Index. The dots in the scatter plot show particular data points, and they can be used to determine patterns. In instances where the horizontal values are given, it becomes easier to predict the vertical value (Ali & Younas, 2021). In the output offered, the distribution of the scatter plots is concentrated in one region. Besides, the distance between the dots is negligible. There is a positive correlation between the variables. There is also a BMI outlier point, which shows that weight may have a higher effect on BMI.

## Conclusion

This assignment has entailed an exploration of an SPSS output showing correlational analysis. Therefore, various aspects have been explored, including mean, standard deviation, and the magnitude of the relationships. In addition, the direction of relationships has also been explored and discussed.

## References

Ali, P., & Younas, A. (2021). Understanding and interpreting regression analysis. Evidence-Based Nursing. https://doi.org/10.1136/ebnurs-2021-103425

Janse, R. J., Hoekstra, T., Jager, K. J., Zoccali, C., Tripepi, G., Dekker, F. W., & van Diepen, M. (2021). Conducting correlation analysis: important limitations and pitfalls. Clinical Kidney Journal14(11), 2332-2337. https://doi.org/10.1093/ckj/sfab085

Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2020). Methods and algorithms for correlation analysis in R. Journal of Open Source Software5(51), 2306. https://joss.theoj.org/papers/10.21105/joss.02306.pdf

Pandey, S. (2020). Principles of correlation and regression analysis. Journal of the Practice of Cardiovascular Sciences6(1), 7-11. Doi: 10.4103/jpcs.jpcs_2_20